Global proteomics approaches have dominated the field of mass spectrometry for the study of proteins and biomarkers. Increasingly, more targeted approaches are being used for understanding biological problems. In the past year or two, a trend towards hypothesis-driven discovery has emerged. Hypothesis-driven approaches can be beneficial in that they typically focus on collecting data that will answer a specific biological question and thus reduce resources used to collect what is often extraneous data. Although more focused, hypothesis-driven approaches are still limited by the number of mass spectrometry scans that can be performed in a single experiment. One approach to maximize the utility of collected data is the Selective Reaction Monitoring (“SRM”) experiment. In a SRM experiment on a triple-quadrupole mass spectrometer, the first quadrupole (Q1) is set to pass ions only of a specified m/z (precursor ions) of an expected chemical species in the sample. The second quadrupole (i.e. Q2 or the collision cell) is used to fragment the ions passing through Q1. The third quadrupole (Q3) is set to pass to the detector only ions of a specified m/z (fragment ions) corresponding to an expected fragmentation product of the expected chemical species. When numerous SRM experiments are run, as is typically the case, the process is called Multiple Reaction Monitoring (“MRM”.) MRM scans have excellent specificity because very few chemical species will share the combination of precursor m/z and fragment m/z values specified. The pair of m/z values is termed the “MRM transition” or alternately, the parent-daughter ion transition pair “PDITP” being monitored. One example of an MRM workflow is the MIDAS workflow (PDITP Initiated Detection And Sequencing), where signal in a PDITP channel will trigger the acquisition of a full scan MS/MS on the parent ion to confirm the peptide identity. The present teachings can enable this, and other MRM-related workflows, with greater efficiency, specificity and sensitivity.
In the MIDAS workflow, a software script can be used to determine Q1 and Q3 masses based on a protein sequence. Basic rules can be used to determine the masses. Generally, no prioritization of PDITPs is performed. Large lists of PDITPs are built which must then be manually curated by user. Many hypotheses can be tested using PDITP transitions but as the proteins get larger or if more proteins need to be tested, the maximum number of PDITPs for the acquisition method can be quickly exceeded. Thus, the script is limited to processing a few proteins at a single time. Because of the simple logic in such scripts, and no effective way of screening for the most likely peptides to be observed or the most intense PDITPs to monitor, the list of PDITP quickly expands. Only up to ˜150 PDITPs can be monitored in a single time period, therefore PDITP transitions to only a few proteins can be built into a single acquisition method. With the number of candidate biomarkers coming out of the biomarker discovery platforms, there is a great need to have a more efficient method of developing mass spectrometry methods for validating which markers are the most promising (the most diagnostic or prognostic). With the present teachings, more efficient PDITP driven discovery methods requiring minimal user curation (such as PDITP driven MS/MS experiments) can be designed.